文章基本信息

标题：Developing Web-based Support Systems for Predicting Poor-performing Students using Educational Data Mining Techniques
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作者：Phauk Sokkhey ; Takeo Okazaki
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：7
DOI：10.14569/IJACSA.2020.0110704
出版社：Science and Information Society (SAI)
摘要：The primary goal of educational systems is to enrich the quality of education by maximizing the best results and minimizing the failure rate of poor-performing students. Early predicting student performance has become a challenging task for the improvement and development of academic performance. Educational data mining is an effective discipline of data mining concerned with information integrated into the education domain. The study is of this work is to propose techniques in educational data mining and integrate it into a web-based system for predicting poor-performing students. A comparative study of prediction models was conducted. Subsequently, high performing models were developed to get higher performance. The hybrid random forest named Hybrid RF produces the most successful classification. For the context of intervention and improving the learning outcomes, a novel feature selection method named MICHI, which is the combination of mutual information and chi-square algorithms based on the ranked feature scores is introduced to select a dominant set and improve performance of prediction models. By using the proposed techniques of educational data mining, and academic performance prediction system is subsequently developed for educational stockholders to get an early prediction of student learning outcomes for timely intervention. Experimental results and evaluation surveys report the effectiveness and usefulness of the developed academic prediction system. The system is used to help educational stakeholders for intervening and improving student performance.
关键词：Academic performance prediction systems; educational data mining; dominant factors; feature selection methods; prediction models; student performance